The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Feature selection and feature extraction for text categorization
Open accessAbstract
The effect of selecting varying numbers and kinds of features for use in predicting category membership was investigated on the Reuters and MUC-3 text categorization data sets. Good categorization performance was achieved using a statistical classifier and a proportional assignment strategy. The optimal feature set size for word-based indexing was found to be surprisingly low (10 to 15 features) despite the large training sets. The extraction of new text features by syntactic analysis and feature clustering was investigated on the Reuters data set. Syntactic indexing phrases, clusters of these phrases, and clusters of words were all found to provide less effective representations than individual words.
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(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Pattern Classification and Scene Analysis.
Grenander, U., Duda, R. O., & Hart, P. E. (1974) · Journal of the American Statistical Association · doi:10.2307/2286028 · Cited by 1,006
A stochastic parts program and noun phrase parser for unrestricted text
Church, K. W. (1988) · Proceedings of the second conference on Applied natural language processing - · doi:10.3115/974235.974260 · Cited by 957
Hamming, R. W. (1980) · Cited by 693
Information Processing and Management
(1984) · Journal of Information Processing and Management · doi:10.1241/johokanri.27.603 · Cited by 275
Evaluating text categorization
Lewis, D. D. (1991) · Proceedings of the workshop on Speech and Natural Language - HLT '91 · doi:10.3115/112405.112471 · Cited by 231
Self-citationELLAS, P. (1959) · Reviews of Modern Physics · doi:10.1103/revmodphys.31.221 · Cited by 223
Models for retrieval with probabilistic indexing
Fuhr, N. (1989) · Information Processing & Management · doi:10.1016/0306-4573(89)90091-5 · Cited by 177
CONSTRUE/TIS: A System for Content-Based Indexing of a Database of News Stories
Hayes, P. J., & Weinstein, S. P. (1990) · Innovative Applications of Artificial Intelligence · Cited by 159
AIR/X - A rule-based multistage indexing system for Iarge subject fields.
Fuhr, N., Hartmann, S., Lustig, G., Schwantner, M., Tzeras, K., & Knorz, G. (1991) · Cited by 101
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Selection of relevant features and examples in machine learning
Blum, A. L., & Langley, P. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00063-5 · Cited by 3,298
Feature Selection: A Data Perspective
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2018) · ACM Computing Surveys · doi:10.1145/3136625 · Cited by 2,442
A review of feature selection methods based on mutual information
Vergara, J. R., & Estévez, P. A. (2014) · Neural Computing and Applications · doi:10.1007/s00521-013-1368-0 · Cited by 1,255
Androutsopoulos, I., Koutsias, J., Chandrinos, K. V., & Spyropoulos, C. D. (2000) · Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/345508.345569 · Cited by 465
MIFS-ND: A mutual information-based feature selection method
Hoque, N., Bhattacharyya, D. K., & Kalita, J. K. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.04.019 · Cited by 442
Metadata
- Authors
- Lewis, D. D.
- Venue
- Proceedings of the workshop on Speech and Natural Language - HLT '91
- Published
- 1992
- Pages
- 212
- OpenAlex
- W2043772506
- Cited by
- 578
- References
- 11
- FWCI
- 4.01
- Type
- conference paper
- Language
- en
- Open access
- bronze
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